EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA723584/SAMN18822748/SRR14295363/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 13421
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 10
max_counts = 25000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 10 %): 4375
## percentage of retained cells: 32.6 %
## cells retained by counts ( 25000 ): 4350
## percentage of retained cells: 32.41 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 300
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## IGKC MALAT1 IGHG1 RPLP1 MT-CO2 IGHG3 RPL18A
## 24.7896774 3.6361290 1.5883871 1.4580645 1.1122581 1.0296774 0.6748387
## B2M MT-CO1 MT-CO3 JCHAIN MT-ATP6 RPS15 RPS28
## 0.6632258 0.6503226 0.6400000 0.6348387 0.6283871 0.6025806 0.5729032
## MT-ND4 RPL32 RPL13 EEF1A1 RPS18 FTL RPL29
## 0.5651613 0.5548387 0.5509677 0.5109677 0.4929032 0.4838710 0.4787097
## RPL41 RPL10 MT-CYB RPS12 RPL37 RPL28 UBA52
## 0.4606452 0.4477419 0.4425806 0.4232258 0.4167742 0.4077419 0.4051613
## TPT1 RPS14
## 0.3987097 0.3935484
## cells retained by counts ( 300 ): 3575
## percentage of retained cells: 26.64 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN18822748_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: STMN1, HMGB2, TUBA1B, NUSAP1, MKI67
## Negative: DNAJB9, IGHG1, TNFRSF17, MT-CO2, ISG20
## PC_ 2
## Positive: DUT, FAM111B, HELLS, PCNA, GINS2
## Negative: CCNB1, PLK1, CCNB2, ARL6IP1, NEK2
## PC_ 3
## Positive: RRM2, HIST1H4C, PCLAF, STMN1, NUSAP1
## Negative: RPL18A, RPS24, HSP90AB1, NCL, RPS2
## PC_ 4
## Positive: RPL7A, IGHG1, RPS18, MIF, RPS23
## Negative: MALAT1, XAF1, NEAT1, TXNIP, MX1
## PC_ 5
## Positive: JCHAIN, HERPUD1, MTDH, DNAAF1, CACYBP
## Negative: TMSB10, B2M, RPL18A, RPS15, LY6E
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers